Road markings refer to the signs drawn on the surface of a road (e.g. speed limits, left-turn, right-turn, crossing, etc.), as opposed to traffic signs erected by the side or on top of roads. Road markings, as traffic signs, are important for navigation systems and driver assistive devices. While many road markings provide information that is redundant to traffic signs (e.g., speed limits), other information, such as arrows for turn-only lanes, are exclusively provided by road markings.
One existing method for road marking detection is based on lane detection techniques, such as the use of edges, regions, and tracking for continuity. For example, one existing lane detection method uses edges detected on an image to identify lane and road boundaries, leading to detection of intersections. The detected lane markers are used to guide road marking detection. However, this conventional method classifies the road markings after detecting them by computing the horizontal extent of the markings at different distances using scan lines. This method requires a lot of hand-coding for each type of road marking and does not scale well to a large number of road marking types.
Another conventional solution uses different methods to detect rectilinear markings and non-rectilinear markings such as arrows. However, this method is too slow to use in real-time for road marking detection. Methods, such as the ones listed above, which rely on lane marking detections, cannot work when lane markings are absent. Additionally, changes in lighting and road surface can affect the performance of the conventional methods. Detecting road markings with multiple disconnected components, especially when they are surrounded by other markings on the road, is an even more challenging task faced by these conventional methods.
Another conventional method generates a number of images of an ideal template under different illumination, scale, blur, and motion blur and matches this set of generated images to the candidate region in a test image. This matching is performed using an eigenspace technique. However, the method is reliant on the proper cropping and centering of the road marking, which does not provide efficient and practical road marking detection when operating in real-time with complex road markings.
The figures depict various embodiments of the invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.